将包含另一个词典列表的词典列表转换为数据框 [英] Convert list of dictionaries containing another list of dictionaries to dataframe

问题描述

我试图寻找解决方案，但无法获得1.我从python中的api获得以下输出.

I tried to look for the solution and I am unable to get 1. I have the following output from an api in python.

insights = [ <Insights> {
    "account_id": "1234",
    "actions": [
        {
            "action_type": "add_to_cart",
            "value": "8"
        },
        {
            "action_type": "purchase",
            "value": "2"
        }
    ],
    "cust_id": "xyz123",
    "cust_name": "xyz",
}, <Insights> {
    "account_id": "1234",
    "cust_id": "pqr123",
    "cust_name": "pqr",
},  <Insights> {
    "account_id": "1234",
    "actions": [
        {
            "action_type": "purchase",
            "value": "45"
        }
    ],
    "cust_id": "abc123",
    "cust_name": "abc",
 }
 ]

我想要这样的数据框

- account_id    add_to_cart purchase    cust_id cust_name
- 1234                    8        2    xyz123  xyz
- 1234                                  pqr123  pqr
- 1234                            45    abc123  abc

当我使用以下

> insights_1 = [x for x in insights]

> df = pd.DataFrame(insights_1)

我得到以下内容

- account_id                                       actions  cust_id cust_name
- 1234  [{'value': '8', 'action_type': 'add_to_cart'},{'value': '2', 'action_type': 'purchase'}]                                    xyz123  xyz
- 1234                                              NaN     pqr123  pqr
- 1234  [{'value': '45', 'action_type': 'purchase'}]        abc123  abc

我该如何进行?

推荐答案

这是一种解决方案.

df = pd.DataFrame(insights)

parts = [pd.DataFrame({d['action_type']: d['value'] for d in x}, index=[0])
         if x == x else pd.DataFrame({'add_to_cart': [np.nan], 'purchase': [np.nan]})
         for x in df['actions']]

df = df.drop('actions', 1)\
       .join(pd.concat(parts, axis=0, ignore_index=True))

print(df)

  account_id cust_id cust_name add_to_cart purchase
0       1234  xyz123       xyz           8        2
1       1234  pqr123       pqr         NaN      NaN
2       1234  abc123       abc         NaN       45

说明

• 利用pandas将字典的外部列表读入数据框.
• 对于内部字典，将列表理解与字典理解结合使用.
• 通过测试列表理解力是否相等来计算nan值.
• 连接零件并将其连接到原始数据框.

• Utilise pandas to read the outer list of dictionaries into a dataframe.
• For the inner dictionaries, use a list comprehension together with a dictionary comprehension.
• Account for nan values by testing for equality within the list comprehension.
• Concatenate and join the parts to the original dataframe.

说明-详细信息

这详细介绍了parts的构造和使用:

This details the construction and use of parts:

1. 获取df['actions']中的每个条目；每个条目将是一个列表 字典.
2. 在for循环中逐行(即逐行)对其进行迭代.
3. else部分说如果它是np.nan [即null]，则返回nan s的数据帧". if部分获取字典列表，并为每行创建一个迷你数据框 .
4. 然后，我们使用下一部分将这些小词典连接起来，每行一个，然后将它们连接到原始数据框.

1. Take each entry in df['actions']; each entry will be a list of dictionaries.
2. Iterate them one by one, i.e. by row, in a for loop.
3. The else part says "if it is np.nan [i.e. null] then return a dataframe of nans". The if part takes the list of dictionaries and creates a mini-dataframe for each row.
4. We then use the next part to concatenate these mini-dictionaries, one for each row, and join them to the original dataframe.

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